arXiv subjects

Chengjian Liu

Publications and source records attributed to Chengjian Liu.

4 recordsLinked to original sources

arXiv2025

AutoFSM: A Multi-agent Framework for FSM Code Generation with IR and SystemC-Based Testing

With the rapid advancement of large language models (LLMs) in code generation, their applications in hardware design are receiving growing attention. However, existing LLMs face several challenges when generating Verilog code for finite state machine (FSM) control logic, including frequent syntax errors, low debugging efficiency, and heavy reliance on test benchmarks. To address these challenges, this paper proposes AutoFSM, a multi-agent collaborative framework designed for FSM code generation tasks. AutoFSM introduces a structurally clear intermediate representation (IR) to reduce syntax error rate during code generation and provides a supporting toolchain to enable automatic translation from IR to Verilog. Furthermore, AutoFSM is the first to integrate SystemC-based modeling with automatic testbench generation, thereby improving debugging efficiency and feedback quality. To systematically evaluate the framework's performance, we construct SKT-FSM, the first hierarchical FSM benchmark in the field, comprising 67 FSM samples across different complexity levels. Experimental results show that, under the same base LLM, AutoFSM consistently outperforms the open-source framework MAGE on the SKT-FSM benchmark, achieving up to an 11.94% improvement in pass rate and up to a 17.62% reduction in syntax error rate. These results demonstrate the potential of combining LLMs with structured IR and automated testing to improve the reliability and scalability of register-transfer level (RTL) code generation.

cs.SE↗

arXiv2025

Higher Satisfaction, Lower Cost: A Technical Report on How LLMs Revolutionize Meituan's Intelligent Interaction Systems

Enhancing customer experience is essential for business success, particularly as service demands grow in scale and complexity. Generative artificial intelligence and Large Language Models (LLMs) have empowered intelligent interaction systems to deliver efficient, personalized, and 24/7 support. In practice, intelligent interaction systems encounter several challenges: (1) Constructing high-quality data for cold-start training is difficult, hindering self-evolution and raising labor costs. (2) Multi-turn dialogue performance remains suboptimal due to inadequate intent understanding, rule compliance, and solution extraction. (3) Frequent evolution of business rules affects system operability and transferability, constraining low-cost expansion and adaptability. (4) Reliance on a single LLM is insufficient in complex scenarios, where the absence of multi-agent frameworks and effective collaboration undermines process completeness and service quality. (5) The open-domain nature of multi-turn dialogues, lacking unified golden answers, hampers quantitative evaluation and continuous optimization. To address these challenges, we introduce WOWService, an intelligent interaction system tailored for industrial applications. With the integration of LLMs and multi-agent architectures, WOWService enables autonomous task management and collaborative problem-solving. Specifically, WOWService focuses on core modules including data construction, general capability enhancement, business scenario adaptation, multi-agent coordination, and automated evaluation. Currently, WOWService is deployed on the Meituan App, achieving significant gains in key metrics, e.g., User Satisfaction Metric 1 (USM 1) -27.53% and User Satisfaction Metric 2 (USM 2) +25.51%, demonstrating its effectiveness in capturing user needs and advancing personalized service.

cs.CL↗

arXiv2023

DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining

Communication scheduling has been shown to be effective in accelerating distributed training, which enables all-reduce communications to be overlapped with backpropagation computations. This has been commonly adopted in popular distributed deep learning frameworks. However, there exist two fundamental problems: (1) excessive startup latency proportional to the number of workers for each all-reduce operation; (2) it only achieves sub-optimal training performance due to the dependency and synchronization requirement of the feed-forward computation in the next iteration. We propose a novel scheduling algorithm, DeAR, that decouples the all-reduce primitive into two continuous operations, which overlaps with both backpropagation and feed-forward computations without extra communications. We further design a practical tensor fusion algorithm to improve the training performance. Experimental results with five popular models show that DeAR achieves up to 83% and 15% training speedup over the state-of-the-art solutions on a 64-GPU cluster with 10Gb/s Ethernet and 100Gb/s InfiniBand interconnects, respectively.

cs.LG↗

arXiv2020

A Quantitative Survey of Communication Optimizations in Distributed Deep Learning

Nowadays, large and complex deep learning (DL) models are increasingly trained in a distributed manner across multiple worker machines, in which extensive communications between workers pose serious scaling problems. In this article, we present a quantitative survey of communication optimization techniques for data parallel distributed DL. We first identify the major communication challenges and classify the existing solutions into three levels, namely the learning algorithm, the system architecture, and the network infrastructure. We present the state-of-the-art communication optimization techniques and conduct a comparative study of seven common lossless distributed DL methods on a 32-GPU cluster with 100Gbps InfiniBand (IB). We show that (1) the DL models with low model intensity (such as BERT and BERT-Large) are difficult to scale out even with the best available lossless algorithm over 100Gbps IB; (2) the system architecture and scheduling algorithms have a critical impact on the scaling property. We conclude the article with discussions on the open issues for further investigations.

cs.DC↗